Human motion prediction using semi-adaptable neural networks

Yujiao Cheng, Weiye Zhao, Changliu Liu and Masayoshi Tomizuka
Conference Paper, American Control Conference, 2019, July, 2019

View Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Human motion prediction is an important component to facilitate human robot interaction. Robots need to accurately predict human’s future movement in order to efficiently collaborate with humans, as well as to safely plan its own motion trajectories. Many recent approaches predict human’s future movement using deep learning methods, such as recurrent neural networks. However, existing methods lack the ability to adapt to time-varying human behaviors. Moreover, many of them do not quantify uncertainties in the prediction. This paper proposes a new approach that uses an adaptable neural network for human motion prediction, in order to accommodate human’s time-varying behaviors and to provide uncertainty bounds of the predictions in real time. In particular, a neural network is trained offline to represent the human motion transition model. Recursive least square parameter adaptation algorithm (RLS-PAA) is adopted for online parameter adaptation of the neural network and for uncertainty estimation. Experiments on several human motion datasets verify that the proposed method outperforms the state-of-the-art approach with a significant improvement in terms of prediction accuracy and computation efficiency.


@conference{Cheng-2019-113166,
author = {Yujiao Cheng and Weiye Zhao and Changliu Liu and Masayoshi Tomizuka},
title = {Human motion prediction using semi-adaptable neural networks},
booktitle = {American Control Conference, 2019},
year = {2019},
month = {July},
} 2019-05-22T08:56:15-04:00